Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.

Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large‐scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar‐based hourly rainfall data, a series of hydrologic‐hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D‐GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km², and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high‐danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high‐danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high‐performance computing hydrodynamic large‐scale flood simulations.

The volume of water stored in seasonal wetlands is a fundamental but difficult to measure variable for developing a physical understanding of wetland behavior. For seasonal wetlands that are a major source of water for rice and fish production, this physical understanding is key to planning for water‐food security and ecosystem services. This study quantified variations in volumetric storage for the numerous seasonal wetlands of northeastern Bangladesh, locally known as “haors.” These haors receive transboundary runoff from densely vegetated and mountainous terrain in India and face persistent monsoonal cloud cover as they become full. We estimated volumetric storage for 13 haors by using extensive remote sensing data on water surface extent and elevation that was complemented with citizen‐contributed gauge data. Assuming a trapezoidal bathymetry, an area‐volume relationship was developed for selected haors. This relationship was assumed to be valid for extrapolating volumetric estimations over all the haors in the region. Results suggested that as haors get filled with the onset of monsoon rains, total estimated storage relative to the lowest observable level varied from 6.5 (±0.4) km³ in May to 30.9 (±2.0) km³ in July (peak of monsoon). Choosing a rectangular bathymetry can lead to 47% higher estimates compared to trapezoidal cross section. Estimating this intra‐annual/interannual increase in storage is important for the region to plan water management policies that balance the human and ecosystem needs. Our analytical approach has potential for application to wetlands worldwide in light of the upcoming Surface Water and Ocean Topography (SWOT) mission.

Despite lakes being a key part of the global water cycle and a crucial water resource, there is limited understanding of whether regional or lake-specific factors control water storage variations in small lakes. Here, we study groups of small, unregulated lakes in North Carolina, Washington, Illinois, and Wisconsin, USA using lake level measurements gathered by citizen scientists and lake surface area measurements from optical satellite imagery. We show the lake level measurements to be highly accurate when compared to automated gauges (mean absolute error = 1.6 cm). We compare variations in lake water storage between pairs of lakes within these four states. On average, water storage variations in lake pairs across all study regions are moderately positively correlated (ρ = 0.49) with substantial spread in the degree of correlation. The distance between lake pairs and the extent to which their changes in volume are correlated show a weak but statistically significant negative relationship. Our results indicate that, on regional scales, distance is not a primary factor governing lake water storage patterns, which suggests that other, perhaps lakes-specific, factors must also play important roles.